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Many black-box adversarial attack algorithms perform attacks on machine learning models based on the transferability of adversarial examples, and the input transformation-based attack is one of the most effective methods. However, existing input transformation methods ignore that each pixel contributes differently to the output of the model, and the focus regions of different models on the same images are similar. Therefore, this paper proposes a targeted data augmentation-based adversarial attack algorithm named MixCam, which augments the input data based on the contribution of each pixel to the prediction result. This is done to enhance the transferability of the crafted adversarial example from the perspective of shifting the regions to which the model pays most of its attention. In addition, this paper proposes further boosting the transferability of the crafted adversarial examples by fusing the class activation maps of multiple models for the input image. Furthermore, the MixCam can integrate other input transformation methods to further boost the transferability of crafted adversarial examples. Extensive experiments on ImageNet demonstrate that MixCam outperforms other state-of-the-art methods in black-box attacks against considered adversarially trained models, with an average increase of 11.7% and 10.7% in attack success rates for single and ensemble attack settings, respectively. • A targeted data augmentation-based black-box adversarial attack method. • Performing adversarial attacks from the perspective of moving the focus region of DNN. • Boosting the transferability of the crafted adversarial examples. • Extensive experiments have demonstrated the effectiveness of our method. [ABSTRACT FROM AUTHOR]

This paper investigates leaderless consensus (LLC) and leader-follower consensus (LFC) issues of multiple Euler-Lagrange systems (MELSs) with uncertain system parameters and input disturbances. Firstly, by utilizing event-based control strategy, event-based consensus protocols are proposed to achieve LLC and LFC, respectively. Then, to confirm that no Euler-Lagrange (EL) system will exhibit Zeno behavior under the above event-based consensus protocols, we show that the low bound of event-triggering interval is strictly positive for any time instants. Distinct with the existing related works, the proposed event-triggered consensus protocols only rely on local sampled state, and are irrelevant to the system parameters. Finally, some simulation examples are presented to illustrate the feasibility of the proposed event-based consensus algorithms. [ABSTRACT FROM AUTHOR]

Few-Shot Class-Incremental Learning (FSCIL)presents an extension of the Class Incremental Learning (CIL)problem where a model is faced with the problem of data scarcity while addressing the Catastrophic Forgetting (CF)problem. This problem remains an open problem because all recent works are built upon the Convolutional Neural Networks (CNNs)performing sub-optimally compared to the transformer approaches. Our paper presents Robust Transformer Approach (ROBUSTA)built upon the Compact Convolutional Transformer (CCT). The issue of overfitting due to few samples is overcome with the notion of the stochastic classifier, where the classifier's weights are sampled from a distribution with mean and variance vectors, thus increasing the likelihood of correct classifications, and the batch-norm layer to stabilize the training process. The issue of CFis dealt with the idea of delta parameters, small task-specific trainable parameters while keeping the backbone networks frozen. A non-parametric approach is developed to infer the delta parameters for the model's predictions. The prototype rectification approach is applied to avoid biased prototype calculations due to the issue of data scarcity. The advantage of ROBUSTAis demonstrated through a series of experiments in the benchmark problems where it is capable of outperforming prior arts with big margins without any data augmentation protocols. [ABSTRACT FROM AUTHOR]

The rapid integration of intelligent processes and methods into information systems in the Artificial Intelligence (AI) era has led to a substantial shift towards autonomous software decision-making. This evolution necessitates robust human oversight, especially in critical domains like Healthcare, Education, and Energy. Human trust in AI plays a vital role in influencing decision-making processes of users interacting with AI. This paper presents VIRTSI (V ariability and I mpact of R eciprocal T rust S tates towards I ntelligent systems), a novel rigorous computational model for human-AI Interaction. VIRTSI simulates human trust states, spanning from overtrust to distrust, through user modelling. It comprises: 1. A trust dynamics representational model based on Deterministic Finite State Automata (DFAs), illustrating transitions among cognitive trust states in response to AI-generated replies. 2. A trust evaluation model based on Confusion Matrices, originating from machine learning and Accuracy Metrics, providing a quantitative framework for analysing human trust dynamics. As a result, this is the first time that trust dynamics have been thoroughly traced in a representational model and a method has been developed to assess the impact of possibly harmful states like overtrust and distrust. An empirical study on the recently launched Large Language Model of generative AI, ChatGPT (version 3.5), provides a radical underexplored AI-generated platform for evaluating the human-AI interaction through VIRTSI. The study involved 1200 interactions of real users as well as AI experts together with experts in two very different domains of evaluation, namely software engineering and poetry. This study traces trust dynamics and the emerging human-AI interaction, in concrete examples of real user synergies with generative AI. The research reveals the vital role of maintaining normal trust states for optimal human-AI interaction and that both AI and human users need further steps towards this goal. The real-world implications of this research can guide the creation and evaluation of user interfaces with AI and the incorporation of functionalities in the development of generative AI chatbots in terms of trust by providing a new rigorous DFA representational method of trust dynamics and a corresponding new perspective of confusion matrix evaluation method of the dynamics' impact in the efficiency of human-AI dialogues. [ABSTRACT FROM AUTHOR]

Three-way clustering provides a variety of models with richer structural features than traditional two-way clustering. However, the structural properties of three-way clustering have not been systematically studied. In this paper, we propose an axiomatic framework to study three-way clustering based on the structural properties of three-way clusters. We categorize three-way clustering models into 16 types organized in five levels according to six axioms. We propose three strategies for three-way clustering approaches: the 2to3WC Strategy, the I3WC Strategy, and the E3WC Strategy. These strategies comprehensively cover existing three-way clustering models. We examine each of the existing methods and incorporate almost all of them into these three strategies. The framework not only summarizes the structural properties and methods of existing studies but also provides inspiration for future research. There are six types of three-way clustering models that have not yet been proposed and require further study. Strategies that have not been applied to each type also suggest possible future research directions. [ABSTRACT FROM AUTHOR]

As the generalization of fuzzy covering, fuzzy β covering can effectively deal with uncertain information in hybrid data. Swarm intelligence algorithm has unique advantages in feature selection. This paper proposes a novel cooperative swarm intelligence feature selection method for hybrid data based on fuzzy β covering and fuzzy self-information. First, the parameterized fuzzy β neighborhood and fuzzy decision in a fuzzy β covering decision information system are defined, and the lower and upper fuzzy approximations are further studied. Fuzzy rough set model exclusively focuses on the information derived from the lower fuzzy approximation within a decision. In actual circumstances, the uncertainty of fuzzy information is linked to not only the lower fuzzy approximation but also the upper fuzzy approximation. Then, by incorporating the lower and upper fuzzy approximations of a decision, fuzzy self-information and relative fuzzy self-information are introduced as two evaluation indicators of feature selection. Next, a forward feature selection algorithm based on heuristic search strategy is designed by means of these two evaluation indicators. Moreover, in order to effectively explore feature subsets to a greater extent, a cooperative swarm intelligent feature selection algorithm based on random search strategy is also designed by combining particle swarm optimization algorithm and cuckoo search algorithm. Finally, an array of experiments is executed to compare two designed algorithms with five other existing feature selection algorithms. Experimental results show that these two designed algorithms achieve a high feature selection rate while improving the classification accuracy of the raw data. [ABSTRACT FROM AUTHOR]

Federated learning (FL) enables clients to collaboratively train a global model while safeguarding the privacy of their respective data. In practical applications, the data heterogeneity across clients introduces competing constraints on the accuracy, fairness and robustness of FL models. However, existing methods that attempt to address all three of these issues perform poorly in scenarios with severe data heterogeneity. To address this limitation, this paper proposes an innovative personalized federated learning (PFL) framework named CosPer. This framework employs an adaptive local aggregation mechanism, where the aggregation weight is determined by the cosine similarity between the global gradient and the local gradient of each client. This mechanism can construct a personalized model for each client, suited to different degrees of the data heterogeneity. In addition, we conduct extensive experiments to evaluate the performance of CosPer on four datasets. The results demonstrate that CosPer outperforms other state-of-the-art PFL methods in terms of accuracy, fairness and robustness. [ABSTRACT FROM AUTHOR]

Solving nonlinear equation systems (NESs) is one of the most important tasks in numerical computing. The NESs usually have multiple roots, and quickly locating their roots in a single algorithm run with a limited number of iterations has always been the algorithm improvement direction. In order to further enhance the efficiency of the existing methods, a density clustering-based differential evolution algorithm (DCDE) for the NESs problem solving is proposed in this paper. Firstly, density-based spatial clustering of applications with noise (DBSCAN) is used to divide the population into clusters and noises, which provides a better direction for population evolution. Secondly, a cluster evaluation factor is proposed, which not only divides the clusters into excellent clusters and non-excellent clusters, but also prevents the migration of excellent clusters and maintain the diversity of populations. Then, a migration strategy and individual generation mechanism are proposed to guide non-excellent clusters to migrate to promising regions. Finally, combined with an archive technique, the individuals which satisfy the solution requirements are archived and randomly initialized to further improve population diversity. To verify the effectiveness of the proposed algorithm, comparative experimental results of the propsoed DCDE with other state-of-the-art algorithms for thirty NESs problems solving show that the proposed DCDE is the most effective algorithm to locate more roots in a single run. [ABSTRACT FROM AUTHOR]

This paper investigates the issue of event-triggered optimal control for saturated nonlinear systems with full state constraints. A smooth function is defined to map the constrained states, then the considered system can be transformed into a state unconstrained system with unknown approximate errors. In addition, a novel event-triggered mechanism is proposed via an adaptive dynamic programming algorithm, which can obtain the optimal control law without constructing the so-called event-triggered HJB equation. Moreover, in order to conquer the difficulty caused by the persistence of excitation conditions, the experience replay technique is utilized to design the critic update law. Meanwhile, it is demonstrated that all signals are uniformly ultimately bounded. Finally, two simulation examples are given to demonstrate the effectiveness of the control strategy. [ABSTRACT FROM AUTHOR]

In this paper, we investigate three different methods to construct a fuzzy sphere. These methods depend on the entities available to form a fuzzy sphere. In the first method, we formulate a fuzzy sphere when the position of the center and the value of the radius are given imprecisely. In the second method, a fuzzy sphere is constructed when the diameter of a fuzzy sphere is known. In the last method, we construct a fuzzy sphere that passes through four given S -type space fuzzy points whose core points are not co-planar. Also, a rotation invariance property of a fuzzy sphere is investigated. In addition, we show that the intersection of a fuzzy sphere with a crisp plane is a fuzzy circle. In a sequel, the idea of a fuzzy cone is developed. The properties concerned with the intersection of a fuzzy cone with a crisp plane are studied sequentially. All the studies are supported with geometrical interpretations and numerical examples. Moreover, we also develop the algorithms to get the membership grade of a number (i) in the diameter form of the fuzzy sphere and (ii) in the four-point form of the fuzzy sphere. [ABSTRACT FROM AUTHOR]

In this paper, an attack-defense framework is proposed for the remote H ∞ state estimation of delayed recurrent neural networks (RNNs). Firstly, an important-data-based (IDB) attack strategy is constructed, which can identify the important packets that play essential roles in the estimation and selectively attack them based on their importance degree from the perspective of the attackers. By targeting the important packets, larger attack damages can be achieved. Then, a resilient state estimator that can resist IDB attacks is developed from the defenders' point of view. Notably, some unrealistic assumptions (e.g., the attacker knowing the system structure and full parameters, the defender knowing the attack rate/parameters) are removed, which makes the proposed method easy to implement. At last, simulation results are presented to show the larger destructive effect of the constructed IDB attack and the efficiency of the proposed resilient H ∞ state estimator. [ABSTRACT FROM AUTHOR]

As spatial computing devices increasingly integrate eye-tracking technology to enhance virtual reality (VR) experiences, the imperative to protect sensitive eye-tracking data against privacy risks, such as user re-identification, has become more pressing. Existing privacy-preserving mechanisms face challenges in balancing the dual demands of privacy and utility in the context of VR applications. This paper presents DPGazeSynth , a novel framework designed to fortify privacy protections while ensuring the utility of eye-tracking data. DPGazeSynth addresses the unique requirements of gaze path synthesis, especially the differentiation between fixations and saccades. Our approach introduces a semi-synthetic method based on the Markov Chain model to accurately maintain data correlations for analytical tasks. We demonstrate that DPGazeSynth provides robust differential privacy guarantees, and our comprehensive experiments on two real-world datasets validate its effectiveness in safeguarding against re-identification attacks. The results showcase DPGazeSynth 's better performance over existing solutions like Kal ε ido and establish its potential as a reliable foundation for future research aimed at reconciling privacy concerns with the demands of complex trajectory data analysis in eye-tracking applications. [ABSTRACT FROM AUTHOR]

Lip reading is a visual alternative to enhance the intelligence of traditional speech recognition, which can benefit from retina-like event cameras that focus on dynamic movements. Spiking Neural Networks (SNNs) are inherently well-suited to cooperate with event cameras. However, employing SNN for event-based lip reading presents significant challenges, particularly in effectively extracting spatio-temporal features from input events and distinguishing between phonetically similar elements. To address these challenges, this paper proposes a novel event-based lip-reading model that leverages the SNN framework, enriched by a designed Spatial-Temporal Attention Block (STAB) and a triplet loss. Specifically, STAB comprises both spatial and temporal attention branches, dynamically emphasizing those spatial and temporal characteristics most reflective of lip movements. STAB also includes a fusion mechanism to integrate these spatial and temporal insights, providing a comprehensive and focused representation of lip movements. In addition, we enhance the model by incorporating triplet loss into the SNN training process, further improving the model's ability to distinguish between visually similar words. Experimental results show the superior performance of our SNN and validate the effect of STAB and triplet loss. We also conduct the energy consumption analysis to affirm the model's energy efficiency. [ABSTRACT FROM AUTHOR]

For hierarchical classification tasks, label relationships can be represented as a hierarchical structure ranging from coarse-grained to fine-grained. Existing hierarchical classifications typically employ a top-down classification approach, which leads to significant inter-level error propagation. Moreover, none of the existing approaches consider the impact of the path weights of different classes on the classification. In this paper, we propose a Hierarchical Classification method with Exponential Weighting of Multi-granularity Paths (HCEWMP), which combines path weights and hierarchical structure to propose a new hierarchical classification framework. Firstly, HCEWMP decomposes the datasets from coarse-grained to fine-grained based on the hierarchical structure and assigns weights to paths by the data distribution. Secondly, two different weighting strategies, probability weighting, and exponential weighting, are considered to calculate the probability of each class. Thirdly, the fine-grained top k classes are selected based on the probability descending order. Finally, HCEWMP obtains the best-predicted class using a random forest classifier. Compared with eight different algorithms on seven datasets, our experimental results demonstrate that the proposed method is effective in addressing the inter-level error propagation problem. The exponential weighting strategy has superior results among the two strategies, further indicating the significance of path weighting in hierarchical classification. [ABSTRACT FROM AUTHOR]

This paper studies the issue of secure state estimation for cyber-physical systems vulnerable to sparse sensor attacks, where attackers might change their attack targets at any time to deteriorate the estimation performance. To address this issue, a Luenberger-like observer is designed to estimate the system state under the attack interference, which employs an improved projection operator and the least square method to handle the switched attacks and introduces a disturbance estimator to enhance the anti-disturbance capability. It is proven that the observation error system can achieve asymptotic stability and dissipative performance, even under the influence of switched sensor attacks and disturbances. Different from the existing results, this method can not only defend against more erratic attacks but also effectively reject disturbances. The conclusion presented is validated through simulation experiments. [ABSTRACT FROM AUTHOR]

Adversarial Training has proved to be an effective training paradigm to enforce robustness against adversarial examples in modern neural network architectures. Despite many efforts, explanations of the foundational principles underpinning the effectiveness of Adversarial Training are limited and far from being widely accepted by the Deep Learning community. Moreover, very few research works investigated the limitations of robust Convolutional Neural Networks beyond the well-known accuracy drop on natural images. In this paper, we describe surprising properties of these models, shedding light on mechanisms through which robustness against adversarial attacks is implemented. We also highlight limitations and failure modes that were not discussed in prior works. Through extensive analyses on a wide range of architectures and datasets, we empirically demonstrate that adversarially-trained Convolutional Neural Networks do not exploit efficiently the model capacity and that the simplicity biases induced by Adversarial Training may lead to undesired behaviors. [ABSTRACT FROM AUTHOR]

• Criteria system construction for selecting GRID suppliers. • Introduction of novel fuzzy sets (SVZs, SVZPRs) combining Z-numbers and CST. • Improved efficiency of S-BWM through SVZPR extension. • Development of TOPSIS with SVZs combining TOPSIS and CST. • Enhancement of decision reliability through PageRank integration in SMCDM. Firms need a reliable decision-making framework to select suppliers who are Green, Resilient, and embrace Inclusive Development (GRID). However, GRID supplier selection is challenged by events that are uncertain and often dependent. Such events affect supplier rankings in a fuzzy decision environment. This paper builds a GRID-based criteria system and applies an enhanced Best Worst Method (BWM) and Technique for Order Preferencing using the Similarity to the Ideal Solution (TOPSIS), with Scenario-Varying Z-numbers (SVZs) and reversed PageRank. The decision criteria are extracted from the literature and confirmed by expert judgement. The SVZs and SVZ Preference Relations (SVZPRs) are developed by combining the Concept of Stratification Theory (CST) with Z-numbers, to reflect the impact of Dependent Uncertain Events (DUEs). Reverse PageRank is then used to find the occurrence probability of the scenarios due to DUEs. BWM is applied in an SVZPR environment to obtain the weights of the decision criteria. TOPSIS, incorporating SVZs, is used to rank the suppliers. An example is used to highlight the enhanced BWM-TOPSIS, to find the priority ranking of the GRID weights. Comparative and sensitivity analyses are conducted to validate the proposed method. [ABSTRACT FROM AUTHOR]

Time series forecasting holds significant value across various application scenarios. However, practical implementations often encounter challenges related to low-quality time series data resulting from system failures or external interference. Unfortunately, existing forecasting methods primarily focus on optimizing model architecture for accurate predictions and overlook the importance of addressing data quality issues. In this paper, we propose optimizing data utilization to enhance model performance based on data quality and introduce the Sample Cooperation with Sample Quality (SCSQ) method to facilitate recurrent neural networks training. Firstly, we define sample quality as the matching degree between samples and model, and suggest using the attention entropy to calculate the sample quality through an attention mechanism. Secondly, we optimize the model's gradient vector based on sample quality. To address optimization training involving samples of different qualities effectively, we propose a more reasonable objective function within our proposed sample gradient conflict optimization module and devise a novel algorithm leveraging approximation techniques along with ADMM algorithm implementation. Through experiments conducted on six datasets, the results demonstrate that SCSQ significantly improves LSTM's performance. In certain cases, it even surpasses the state-of-the-art models. Additionally, SCSQ enhances the anti-interference ability of LSTM against low-quality samples. [ABSTRACT FROM AUTHOR]

Large-scale multiobjective optimization problems (LSMOPs) have exponential growth in the search space as the decision variables increase, and the vast search space poses a challenge to the performance of multiobjective evolutionary algorithms (MOEAs). Many current large-scale MOEAs need to consume a large amount of computational resources to get good performance. This paper proposes a two-stage direction-guided evolutionary algorithm for large-scale multiobjective optimization (LMOEA-S2D) to balance the performance and computational resource overhead. The algorithm exploits the Pareto-optimality property of domination and the diversity-preserving property of decomposition to optimize the performance in the two stages, respectively, and designs a corresponding direction-guided mechanism to improve search efficiency. LMOEA-S2D designs global direction search and local direction search in the domination-based stage for efficient exploitation to accelerate population convergence. To promote greater population diversity, a hybrid direction search was devised to aid diversity exploration in the decomposition-based stage, and this facilitates even distribution of candidate solutions across the Pareto optimal frontier. LMOEA-S2D is compared with five state-of-the-art large-scale MOEAs on some large-scale multiobjective test suites with 100 to 5,000 decision variables. The experimental results show that LMOEA-S2D significantly outperformed all compared algorithms under limited computational resources. [ABSTRACT FROM AUTHOR]

In addressing complex and dynamic meteorological disaster decision-making environment, the traditional multi-attribute group decision-making domain model is often unable to effectively deal with the correlation between attributes and the mutual influence of group opinions. To overcome this challenge, this paper proposes a novel quantum framework for group decision-making, which is used to deal with the emergency situation of meteorological disaster. The model initially characterizes attribute correlations using 2-additive Choquet integrals and employs Dempster-Shafer evidence theory to both integrate information and ascertain attribute weights, and decision makers' weights are calculated based on grey relative correlation. On this basis, a quantum-like Bayesian network is developed to capture the interference among decision-makers' opinions. The alternatives are ranked by quantum probabilities computed based on Bayesian principle. Finally, a case study on meteorological disaster emergency scenario assessment is conducted to validate the proposed model's effectiveness and superiority. Additionally, its stability and practicality are confirmed through sensitivity analysis and comparative analysis. • Propose an innovative multi-attribute quantum group decision-making model, reflecting the mutual influence of group opinions. • Adopt a 2-additive Choquet integral based on Mobius transformation to handle the correlation between attributes. • Apply grey relative correlation analysis to determine decision makers' weights. • Employ D-S evidence theory to reconcile conflicting evidence to determine attributes' wrights. [ABSTRACT FROM AUTHOR]

This paper pays attention to the output tracking control problem of nonlinear systems where the output is uniquely measurable and subject to an intermittent constraint. A multiplicative transformation based on the shifting-replaying function is introduced together with barrier Lyapunov function to solve the intermittent output constraint. A state observer independent of the matching condition of control gains is designed via the non-uniform state transformation to relax the assumption of full-state measurability. By virtue of the estimated states, we propose an output feedback controller that only involves one fuzzy adaptive parameter to restrain the effect of unknown system nonlinearities. Through Lyapunov stability analysis, it is proved that the proposed controller can make all signals in the closed-loop system remain semi-globally uniformly ultimately bounded. Two simulation examples are provided to verify the effectiveness of the proposed method, and also the superiority over the existing ones. [ABSTRACT FROM AUTHOR]

Information exchange among individuals is one of the key factors leading to formation of modular structures in social networks, often referred as communities. In this paper, we devise a novel information diffusion based approach to leverage the latent information exchange among the individuals for identifying communities. Designed an algorithm called PraSar, which has mainly two phases namely seeding and unification. In seeding phase, closely connected groups of nodes called seed communities are identified by analyzing the cascades obtained during information diffusion. In unification phase, the external connections of seed communities are reduced by unifying two or more seed communities. Involvement of cascade that are formed during the diffusion process enables PraSar to operate locally and giving an overall complexity of O (n 2). The theoretical foundation of the proposed approach is established by various theorems. Empirical results on diverse real world datasets are evident for the effectiveness and competitiveness of the PraSar algorithm over state-of-the-art community detection algorithms. • A new community detection algorithm based on cascading is proposed. • Seed communities generated subsequently with cascading. • Novel properties proposed to address monster community formation and over-partitioning. • Experimental results show that the algorithm performs well on diverse networks. [ABSTRACT FROM AUTHOR]

Today, more and more data is collected and stored by different organizations. When the data is distributed among multiple users who wish to perform data mining on the joint data, outsourcing the task to cloud servers becomes an attractive solution for users who lack professional skills. However, privacy concerns make users reluctant to adopt such solutions. In this paper, we propose a privacy-preserving outsourced k -means clustering (PPOKC) algorithm on distributed data. Our system consists of multiple users and two non-colluding servers. Each user submits their data to the servers, which perform k -means clustering over the joint data without compromising data privacy. Unlike existing solutions, which typically include a cryptographic server and a computing server, each server in our system provides both services. Based on this architecture, we first design several important sub-protocols, including secure comparison, secure minimum and secure division. We then use these protocols to construct an efficient and highly secure PPOKC algorithm. The implementation of our algorithm relies heavily on secret sharing techniques, complemented by homomorphic encryption. We also conduct theoretical analysis and experiments. Security analysis shows that our scheme guarantees the security of the input/output data, the intermediate results and the data access pattern under the semi-honest model. Complexity analysis and numerical experiments show that our algorithm has good efficiency and is suitable for practical applications. [ABSTRACT FROM AUTHOR]

Traditional multi-criterion decision-making methods can help decision-makers decide whether to accept or reject an alternative, but they cannot ensure these results fully meet decision-makers' requirements. Three-way decision methods overcome this limitation by dividing alternatives into three regions. However, traditional three-way decision methods rarely considered the reliability of evaluation information and individual regrets under some criteria. Moreover, most weighting methods in three-way decisions ignored the influence of abnormal values. To fill the above gaps, this paper develops a linguistic Z-number-based sorting method combining the three-way decision with the gain and lost dominance score (GLDS) method that reflects individual regrets under some criteria. Firstly, to avoid individual regrets, we introduce the linguistic Z-number-based GLDS sorting (LZ-GLDS-Sort) method, which obtains two states of alternatives that will be used in the three-way decision. Linguistic Z-numbers can express linguistic evaluation and credibility. Two parameters are introduced in the LZ-GLDS-Sorting method to reflect the relative importance of evaluation information and credibility. Then, a three-way decision method is introduced to divide alternatives into three regions, in which the variation-coefficient weighting method is introduced to avoid the effect of abnormal values. Finally, the ecological function zoning as an example is provided to validate the applicability of this model. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of prescribed-time fault-tolerant tracking control for a class of Markov jump nonlinear systems affected by actuator faults and input saturation. First, a new time-varying constraint function and an error transformation are invoked to transform the original prescribed-time tracking control problem into a tracking control problem with deferred constraint for tracking error. Furthermore, the parameter correction terms are designed, on which a novel Markov jump saturation dynamic filter is constructed to handle the unfavorable effect of input saturation. Meanwhile, the projection operator technique and the norm estimation methodology are integrated to devise adaptive laws to estimate the unknown actuator faults evolving in accordance with the Markov chain. A new prescribed-time fault-tolerant tracking control scheme is put forward that realizes the deferred constraint on the tracking error, which in turn ensures that the tracking error of the system converges to a prescribed accuracy within a prescribed-time. Finally, two simulation examples, one of which implements a prescribed-time fault-tolerant tracking control for rotary steerable drilling tool system, are presented to illustrate the effectiveness and practicality of the developed scheme. [ABSTRACT FROM AUTHOR]

This paper introduces a novel method called cascaded maximum median-margin discriminant projection (CMMDP) for robust face recognition. Initially, we introduce the maximum median-margin discriminant projection (MMDP) method, which aims to maximize the distances between class medians and their inter-class neighbors, while simultaneously minimizing the distances between class medians and their intra-class samples. To extract features at multiple levels, we extend the MMDP method to its deep version, known as CMMDP. The CMMDP algorithm incorporates the MMDP technique to learn multistage filter banks specifically designed for facial images. Afterward, we utilize straightforward binary hashing techniques for efficient indexing, along with block histograms for effective feature pooling. The CMMDP model is simple and easy to train, while taking full account of the global and local structure of the samples by maximizing the distances between samples that are dissimilar and minimizing the distances between samples that are similar after projection. This results in a low-dimensional data representation with better discriminative performance. We evaluate the performance of CMMDP on face datasets including AR, ORL, ExtYaleB and CMU PIE databases. The results indicate that CMMDP achieves superior performance than most advanced methods. [ABSTRACT FROM AUTHOR]

This paper introduces a decentralized and mixed performance-constrained state-estimated fuzzy control strategy designed for nonlinear differential-algebraic interconnected systems. Initially, the approach employs a Takagi-Sugeno (T-S) fuzzy model to approximate these complex systems. To address potential limitations within the interconnected systems, a Proportional-plus-Derivative (PD) state control method is integrated into the proposed control scheme. Additionally, an observer is constructed to estimate system states, enhancing the feasibility of the control method. System stability analysis is conducted using the free-weighing Lyapunov function approach, allowing designers to customize the weight matrix for desired control performance and facilitate controller design. Subsequently, a fuzzy controller is developed to ensure stability and meet both Passive- H ∞ Performance (PHP) and H 2 Control Performance (HCP) criteria. Finally, the effectiveness of the proposed design approach is validated through simulation results. [ABSTRACT FROM AUTHOR]

Type-2 fuzzy neural networks (T2FNNs) have gained popularity due to their processing ability for high uncertainty. However, concerned with the high-dimensional problems of nonlinear systems, the interpretability of individual T2FNNs is weak due to the exponential growth of fuzzy rules. To deal with this problem, an information orientation-based modular T2FNN (IO-MT2FNN) is developed to improve its interpretability in this paper. First, an information entropy-based decomposition method is designed to divide the original input space into three sub-spaces, namely edge, local and global regions. Then, the information with different attributes is separated to provide an unambiguous interpretation. Second, the independent module describing these regions with type-2 fuzzy sets is embedded in the membership function layer of IO-MT2FNN to represent the coupling relationship between regional information in an interpretable way. Third, an information mapping strategy is introduced with low-order Gaussian kernel matrices, instead of a high-order mapping matrix, to extract the features from the allocated information in each module, which enables IO-MT2FNN to achieve a compact topology through dimensionality reduction. Finally, the simulations demonstrate that the proposed IO-MT2FNN can compete with the advanced approaches in terms of interpretability for the prediction of high-dimensional and complex systems. [ABSTRACT FROM AUTHOR]

The Frequent Itemset Hiding Problem (FIHP) constitutes a critical aspect of Privacy-Preserving Data Mining, aiming to protect sensitive information while extracting valuable patterns. Despite the ubiquity of Knowledge Graphs (KGs) in many domains, their integration with FIHP remains an underexplored research area. In this paper, we present a new solution to FIHP, leveraging graph-based algorithms and Community Detection. Our method employs the efficiency and structure of KGs to quickly locate what needs to be hidden in order to strategically conceal sensitive knowledge within the data, while ensuring the preservation of KG structure and utility. Through comprehensive evaluations on established FIHP datasets, we showcase the efficacy of our approach in terms of processing time and increased data privacy. The results imply that our proposed method holds promise for facilitating privacy-preserving KG analytics in real-world applications, particularly in scenarios involving substantial streaming data, where existing approaches encounter limitations. [ABSTRACT FROM AUTHOR]

Data dependencies are a key concept in data management and have been researched in data integration, data quality and query optimization. With the increasing use of graph-structured data in diverse applications, there is also an increasing interest in the study of graph data dependencies. In this scenario, different classes of graph data dependencies have been proposed in the literature. In this work, we study the class of Graph Generating Dependencies (GGDs). Graph Generating Dependencies (GGDs) informally express constraints between two (possibly different) graph patterns which enforce relationships on both graph's data (via property value constraints) and its structure (via topological constraints). While most of previously proposed classes of graph data dependencies focus on generalizing equality-generating dependencies for graph data, Graph Generating Dependencies (GGDs) can express tuple- and equality-generating dependencies on property graphs, both of which find broad application in graph data management. Given this new class of dependency, in this paper, we discuss the reasoning behind GGDs on Property Graphs. We propose algorithms to solve three main reasoning problems: the satisfiability , implication , and validation problems for GGDs and analyze their complexity. By studying these problems, we can understand the expressiveness and the limitations of GGDs in practical applications. To demonstrate the practical use of GGDs, we propose an algorithm that finds inconsistencies in data through validation of GGDs. Our experiments show that even though the validation of GGDs has high computational complexity, GGDs can be used to find data inconsistencies in a feasible execution time on both synthetic and real-world data. • A new class of dependencies for property graphs named Graph Generating Dependencies (GGDs) is introduced. • Study is performed on the reasoning problems: satisfiability, implication and validation on property graphs with GGDs. • A Chase procedure for GGDs to solve the problems of satisfiability and implication is proposed. • Experimental results show the feasibility of using GGDs to identify data inconsistencies in practice. [ABSTRACT FROM AUTHOR]

Clustering analysis is a powerful tool for discovering potential knowledge in datasets. However, numerous existing clustering algorithms suffer from heavy reliance on parameter settings and cannot cluster complex manifold data very well. Moreover, although non-parameter clustering algorithms aim to lower the usage threshold, their actual performance in clustering is often unsatisfactory. Addressing how to achieve similar clustering effects for numerous data points with only a few core data points during clustering is a valuable consideration. To alleviate these challenges, a non-parameter clustering algorithm is proposed and named N on- p arameter C lustering Algorithm based on C hain P ropagation and N atural Neighbor (NPCCPN) in this paper, by jointly using chain propagation and natural neighbor. Specifically, NPCCPN identifies core points through chain propagation and clusters them using the saturated neighborhood graph. This makes the core data extraction and clustering process efficient and non-parameter. Finally, the performance of the method is validated on 15 complex synthetic datasets and 10 real datasets from public UCI database. The experimental results show that the effectiveness and superiority of the proposed algorithm. Purity scores first. ACC scores second without parameters, but the score first algorithm needs to set the parameters. [ABSTRACT FROM AUTHOR]

Clustering, as an essential technique in unsupervised learning, plays a pivotal role in the fields of data mining and machine learning. However, the classic K -means clustering algorithm has intrinsic drawbacks such as sensitivity to initial cluster centers, susceptibility to a local optimal solution, and challenges in handling data uncertainty. To address these problems, this paper proposes an artificial hummingbird algorithm (AHA)-based three-way K -means clustering algorithm, called AHA-3WKM. First, AHA is introduced to address the problems of sensitivity to initial cluster centers and local optima. Second, a fitness function of AHA is specifically constructed to find the best initial clustering centers so that the hummingbirds can search for high-quality food sources, i.e., the global optimum cluster centers. Third, a three-way clustering approach is utilized to capture information about data uncertainty. In this way, the results of clustering are divided into three distinct regions based on the relationship between objects and clusters. The experimental results demonstrate that AHA-3WKM has good performance, and enhances the stability and the accuracy of clustering results. • AHA is introduced to address the problems of the sensitivity to initial cluster centers and the proneness to local optima. Hummingbirds are treated as data points, which dynamically update their strategies and effectively find the optimal cluster centers during multiple iterations. • A fitness function is designed based on the clustering principle of "birds of a feather flock together", with the aim of simplifying calculations, which enhances the specificity and practicality of K-means algorithm. • An AHA-based three-way K-means clustering algorithm (i.e., AHA-3WKM) is proposed. The clustering process is initialized with cluster centers optimized by AHA, and the results are represented in three regions, which can capture the uncertainty within the datasets. [ABSTRACT FROM AUTHOR]

This paper studies the adaptive event-triggered (AET)-based path following controller design problem for a networked autonomous vehicle, where the limited networked bandwidth, system nonlinearities and uncertainties are considered. The polytopic model is proposed to construct a linear parameter varying (LPV) system for the nonlinear vehicle system. Taking into account the effects of event trigger on the path following performance, an improved AET mechanism is designed to achieve a balance between efficient communication and accurate path following requirements. Sufficient conditions are developed to guarantee that the closed-loop system is exponentially stable with an H ∞ performance index being satisfied. In addition, a co-design method with less conservatism is derived to obtain the event-triggered weight matrix and output feedback controller gain matrix simultaneously. Finally, simulation results verify the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

In this paper we propose an aspect-based recommendation model based on variational autoencoders, that provides not only coarse predictions about what items users may like, but also finer-grained predictions of specific aspects users may be interested in of the recommended items. The proposed model first employs convolution operations to learn probability distributions of aspect-level embeddings for users and items from user aspect-level sentiments on seen items. Then it samples from these distributions and feeds the samples into transpose convolution operations to 'reconstruct' missing aspect-level sentiments for unseen items, thereby serving as signals for generating recommendations. To prevent overfitting, we impose a prior on the representation distributions and penalize the model if the learned distributions diverge from the prior. Based on the output of the proposed model, we propose a two-stage ranking scheme that combines aspect-level and overall sentiment signals to rank items. Experiment results show that the proposed model outperforms state-of-the-art aspect-based recommendation models, and the two-stage ranking scheme improves the traditional ranking by overall sentiment predictions. [ABSTRACT FROM AUTHOR]

Time-series representation learning is crucial for extracting meaningful representations from time-series data with temporal dynamics and sparse labels. Contrastive learning, a powerful technique for exploiting the inherent data patterns, has been applied to explore the diverse consistencies in time-series data, achieved through careful selection of contrastive pairs and design of appropriate loss. Encouraging such consistency is essential for acquiring comprehensive representations of time-series data. In this paper, we propose a new framework for time-series representation learning that combines the advantages of contextual, temporal, and transformation consistencies. This framework enables the network to learn general representations suitable for different tasks and domains. First, positive and negative pairs are generated to establish a multi-task learning setup. Then, contrastive losses are formulated to explore contextual, temporal, and transformation consistencies, which are jointly optimized to learn general time-series representations. In addition, we investigate an uncertainty weighting approach to enhance the effectiveness of multi-task learning. To evaluate the performance of our framework, we conduct experiments on three downstream tasks: time-series classification, forecasting, and anomaly detection. The experimental results demonstrate the superior performance of our framework compared to benchmark models across different tasks. Furthermore, our framework shows efficiency in cross-domain transfer learning scenarios. • A new multi-task self-supervised time-series representation learning framework is proposed. • Our method efficiently intergrates contrastive learning approaches for contextual, temporal, and transformation consistency. • The proposed method can learn general representations for time-series classification, forecasting, and anomaly detection. [ABSTRACT FROM AUTHOR]

Sensor nodes (industrial nodes) clustering is an efficacious method for gaining local working conditions real-timely in the production process. The existing deep graph neural networks-based industrial nodes clustering studies construct static graphs to describe the potential spatial correlation among industrial nodes, but the temporal correlation among industrial nodes is ignored, resulting in inferior real-time performance. Meanwhile, these deep learning models fail to obtain good clustering results due to the large-scale and noise-containing characteristics of industrial nodes. In this paper, a large-scale graph clustering method (LsGCM) is proposed for cell conditions spatio-temporal localization in aluminum electrolysis to address the above issues. Specifically, a spatio-temporal graph construction method based on industrial process mechanism knowledge and local working condition boundary types statistical feature fusion is proposed for capturing effective spatio-temporal correlations between industrial nodes. Then, hysteresis knowledge of industrial production system is used to guide the nodes merging, thereby avoiding the incorrect merging of time-asynchronous nodes when performing the spatio-temporal region detection of local working conditions. Finally, combined with the actual spatio-temporal location information of industrial nodes, a novel spatio-temporal information expansion method (StIEM) of the sub-spatio-temporal graph describing local working condition is proposed for obtaining spatio-temporal feature information of the neighborhood, which is beneficial to improve the performance of industrial node clustering. Validation studies on a numerical simulation example and multiple real aluminum electrolysis industrial datasets certificate the effectiveness and superiority of the proposed method. In particular, compared with existing graph clustering methods, our method improves the clustering accuracy by 7.79%. • A large-scale graph clustering method for cell conditions spatio-temporal localization in aluminum electrolysis is proposed. • A spatio-temporal graph adaptive sparsization method based on mechanism knowledge and statistical feature fusion is proposed. • A spatio-temporal information expansion method based on the actual spatio-temporal location of nodes is proposed. [ABSTRACT FROM AUTHOR]

The classification of hazard is critical in industrial informatics, as it enhances early safety alerts, supports decision-making, and facilitates policy assessment. However, previous studies have generally neglected the temporal attributes of hazards, thereby constraining the effectiveness of models. This paper introduces a new model for hazard classification termed DLGM. DLGM represents a deep learning framework, with the structural parameters of grey models to encapsulate the hazard temporal attributes. To better accommodate the fluctuations of hazard series, a new grey model termed FSGM(1,1) equipped with Fourier series is proposed. Moreover, DLGM leverages a novel hierarchical feature fusion neural network (HFFNN) to optimize feature processing. Extensive experiments across three hazard themes involving 18 large-scale industrial processes have demonstrated the competitiveness of DLGM (e.g., it surpasses benchmark models such as Random Forest and BERT by approximately 2% in both accuracy and F1), the suitability of FSGM(1,1) (e.g., its mean absolute percentage error is less than 10% and mean squared error is below 0.02) and the effectiveness of HFFNN (e.g., it enhances the accuracy and F1 of DLGM by about 1%). [ABSTRACT FROM AUTHOR]

• First introduced PD control theory for optimizing learning rate in CNN models. • Designed of innovative center selection and elite guidance strategies. • Developed an efficient enhanced battle royale algorithm(EBRO). • Proposed a new EBRO-ICNN model for air quality prediction. PM 2.5 concentration predictions can provide air pollution control, management, and early warning. However, the PM 2.5 data with high-dimensionality, complexity, and dynamics pose a great challenge to achieve optimal prediction results. Convolutional Neural Networks (CNNs) has unique advantages in processing complex data and contributes to state-of-the-art performances. However, designing the architecture and selecting the learning rate for CNN are time-consuming and requires prior knowledge. Evolutionary algorithms, with the advantages of global convergence, ergodicity, robustness and adaptability, are the most commonly used methods to design the optimal framework for CNNs. Therefore, to improve the predictive performance of CNNs, this paper proposes an improved CNN method (EBRO-ICNN) which employs the enhanced battle royale optimization (EBRO) algorithm and proportional-derivative (PD) control. Firstly, the EBRO algorithm with multistrategy collaborative optimization is introduced, and validated by CEC2017 benchmark functions, which demonstrates EBRO strong global exploration capability, fast convergence speed, and low time complexity. Next, PD control is applied to adjust the learning rate of CNN (ICNN) dynamically, which improves the efficiency and stability of the network training process. At the same time, the ICNN model is optimized using the EBRO algorithm, which can reduce the human interference, generate the optimal framework automatically and enhance the prediction accuracy effectively. Finally, a private air quality dataset and two public datasets are utilized to evaluate the performance of the EBRO-ICNN model, considering 3 error evaluation metrics and 7 prediction comparison models. The experimental results demonstrate that the EBRO-ICNN model exhibits great accuracy and stability. [ABSTRACT FROM AUTHOR]

• An improved gaining-sharing knowledge-based algorithm named MSGSK is proposed. • A parameter adjustment strategy is developed to adjust relative parameters of MSGSK. • A backtracking differential mutation strategy is designed to enrich the population diversity. • A strategy selection mechanism is presented to integrate the former two strategies. • MSGSK extracts more accurate parameters for five PV models. The determination of photovoltaic (PV) model parameters has essential theoretical and practical significance for the performance evaluation, power monitoring, and power generation efficiency calculation of PV systems. In this paper, a multi-strategy gaining-sharing knowledge-based algorithm (MSGSK) is developed to determine these parameters. In our previous work, it has been demonstrated that gaining-sharing knowledge-based algorithm (GSK) is well suited for solving the concerned problem. To enhance its performance, a parameter adjustment strategy is developed to adjust the knowledge rate and knowledge ratio of GSK. Besides, a backtracking differential mutation strategy by combining the mutation scheme of differential evolution and the updating scheme of backtracking search optimization algorithm is developed to enrich the population diversity. Furthermore, a strategy selection mechanism is introduced to integrate the former two strategies to balance exploration and exploitation in different stages of the evolutionary process. The suggested MSGSK algorithm is applied to five PV cases (SDM, DDM, Photowatt-PW201, STM6-40/36, and STP6-120/36). From the experimental data, it can be observed that MSGSK extracts the PV model parameters more precisely than the basic GSK. Furthermore, it exhibits faster convergence speed and higher accuracy compared to other advanced algorithms found in the literature. [ABSTRACT FROM AUTHOR]

Large-capacity machine learning models are vulnerable to membership inference attacks that disclose the privacy of the training dataset. The privacy concerns posed by membership inference attacks have inspired many defense strategies. Unfortunately, they have various limitations: 1) failing to achieve a high-quality tradeoff between membership privacy and model utility. 2) Providing little flexibility to users. This paper proposes Repair-Membership Learning (RM Learning), a simple but effective defense mechanism. RM Learning mitigates membership inference attacks by giving each sample of the training dataset an appropriate soft label to reduce the loss gap between the training and testing datasets of the model. Meanwhile, RM Learning controls the distinguishability between the training and testing datasets by limiting the model's loss gap over them with an adjustable parameter λ to give users flexibility. We evaluated the RM Learning algorithm on four datasets with seven models and compared it with three state-of-the-art methods. For instance, we reduce the effectiveness of black-box and white-box membership inference attacks to about 50.0%, with a loss of less than 1.3% of testing accuracy, for the CIFAR10 dataset with the ResNet18 model. [ABSTRACT FROM AUTHOR]

Fire accidents continually threaten lives and property, posing a great risk to public safety. Therefore, timely, accurate fire smoke and flame detection technology provides a technical foundation for ensuring personnel and property safety. This paper proposes a deep transfer learning model that integrates attention mechanisms and pruning techniques into the DenseNet network (P-DenseNet-A-TL) for detecting fire smoke and flame targets. To reduce the computational complexity of the fire detection model, we simplified the DenseNet network structure. To improve the recognition accuracy of the fire detection model, we incorporated attention mechanisms including a channel attention module (CAM) and spatial attention module (SAM) into the pruned dense network structure. To expedite the model training process, we also introduced a transfer learning model. Furthermore, to enhance the generalization ability and robustness of the model, we created a large-scale fire smoke and flame dataset. The experimental results show that our model (P-DenseNet-A-TL) achieved a test accuracy of 99.06%, F1 score of 99.09%, area under the curve (AUC) of 0.97, and a detection speed of 756 frames per second (FPS). The comparison experimental results and ablation experimental results indicate that our method achieves high detection accuracy and efficiency. Additionally, it possesses strong generalization capability and robustness. [ABSTRACT FROM AUTHOR]

As the key step of anomaly detection for multivariate time-series (MTS) data, learning the relations among different variables has been explored by many approaches. However, most existing approaches overlook the heterogeneity among variables, that is, different types of variables (continuous numerical variables, discrete categorical variables or hybrid variables) may have different edge distributions. In this paper, we propose a novel semi-supervised anomaly detection framework based on a heterogeneous feature network (HFN) for MTS. Specifically, we first combine the embedding similarity subgraph generated by sensor embedding and the feature value similarity subgraph generated by sensor values to construct a time-series heterogeneous graph, which fully utilizes the rich heterogeneous mutual information among variables. Then, a prediction model containing nodes and channel attentions is jointly optimized to obtain better time-series representations. This approach fuses the state-of-the-art technologies of heterogeneous graph structure learning (HGSL) and representation learning. Experimental results on four sensor datasets from real-world applications demonstrate that our approach achieves more accurate anomaly detection compared to baseline methods, laying a foundation for the rapid positioning of anomalies. [ABSTRACT FROM AUTHOR]

In this paper, we propose generalizations of the EI index to quantify the distinction between intragroup and intergroup connections in social networks. These generalizations enable the analysis of interactions between non-disjoint groups, as large-scale disjoint groups are rarely found in many empirical networks. By expanding social network analysis, these measures facilitate the identification of actors with greater similarities or differences, generating previously untapped knowledge. Furthermore, we propose incorporating both edges' and nodes' weights in assessing the EI index. Intuitively, our approach classifies each network edge as internal or external, depending on the attribute groups that each node linked by the given edge belongs to. Since nodes can belong to more than one attribute group, three methods of classifying a network edge are proposed which depend on the number of shared groups by the pair of nodes. We evaluate the new measures in two distinct network contexts. The first context involves a co-authorship network, where researchers, acting as network actors, are categorized based on their respective areas of expertise in management engineering. The second network consists of trade relations among countries in the Americas, with countries grouped according to their participation in trade agreements. The analysis showed which area of management engineering is more independent by having more internal connections and made it possible to investigate the role of trade agreements in the actual trades made by countries. These proposals provide a more comprehensive and nuanced understanding of network dynamics, allowing for a more precise comprehension of interactions and patterns present in social systems. [ABSTRACT FROM AUTHOR]

With the advance of deep learning, adversarial attack and defense has becoming a hot research topic. However, existing defense methods rely on the prior knowledge of the adversarial attacks, and are also vulnerable to adaptive attacks. In this paper, towards a secure automatic speaker verification system, a novel attack-agnostic defense method is proposed, named by SampleShield , which consists of a pairwise random downsampling (PRD) module and an upsampling module achieved by speech super-resolution (SSR). The PRD module can destroy the adversarial perturbations by downsampling, which also introduces randomness and non-differentiability, leading to its resistance to adaptive attacks. PRD does not use any adversarial example as training data, which makes it an attack-agnostic defense method with good generalization ability. Furthermore, the upsampling module achieved by a neural network can recover the downsampled speech to its original quality. In summary, given the experimental results and analysis on the benchmark public datasets, by fusing the downsampling and upsampling modules, SampleShield achieves excellent performance. On adversarial defense, SampleShield obtains an error rate about 5.2% which is quite close to the idea lower bound value 3.2%. On speech quality, SampleShield gets a 5 dB signal-to-noise ratio improvement with respect to the best existing method. • An adversarial defense framework by fusing downsampling and upsampling. • Pairwise random downsampling to resist normal and adaptive adversarial attacks. • A new deep learning architecture to perform speech super-resolution. [ABSTRACT FROM AUTHOR]

The use of artificial intelligence for decision support and automation has shown tremendous potential in many areas. The ability to explain the decisions made by a machine learning algorithm is fundamental to facilitating the widespread use of this type of tool. There are many important real-world problems where the cost of the decisions depends on the characteristics of each example: these are called example-dependent cost (EDC) problems. For this type of classification problem, an appropriate formulation that takes into account the decision costs is fundamental both for the design of the classifier and for the explanation of its decisions. In this paper, we propose COCOA, an explanation method designed for EDC problems based on a Bayesian discriminant. The proposed method can provide counterfactual samples generated by considering decision costs. The COCOA method provides valid and plausible counterfactuals with a high success rate, which can be actionable, diverse, and sparse, achieving a remarkable improvement in terms of cost over five state-of-the-art methods on six real-world datasets. • COCOA provides actionable, plausible, sparse and diverse counterfactuals. • Decision costs are taken into account when generating counterfactuals. • The obtained counterfactuals improve cost savings. • The method is presented for MLPs, but other architectures can be used. [ABSTRACT FROM AUTHOR]

This paper addresses the sliding mode secure control problem for continuous-time cyber-physical systems (CPSs) subject to unmatched disturbances and actuator deception attacks. To capture different operating conditions and account for random component failures in CPSs, a Markov jumping model is introduced to characterize the system's component matrices. Next, a novel model-free integral sliding mode function and a corresponding sliding mode controller are presented to mitigate the impact of external disturbances and deception attacks on Markov jumping cyber-physical systems. An algorithm based on adaptive dynamic programming (ADP) and homotopic policy iteration (PI) is applied to develop the sliding mode controller, called the online homotopic ADP algorithm, which avoids the requirement for an initial stabilizing matrix compared to existing PI algorithms. Lastly, the mode-independent integral sliding mode control scheme is studied to a two-mode Markov jumping vertical take-off and landing helicopter system, providing guaranteed reachability of sliding variables and stability of sliding mode dynamics. Importantly, this is achieved without any reliance on knowledge of system dynamics. [ABSTRACT FROM AUTHOR]

The G-Skyline query aims to return the best groups that are not g-dominated by any other group of equal size; this approach plays an important role in many fields, such as multiobjective decision-making. The top- k G-Skyline query has important implications since the output size of the G-Skyline query is often too large to make decisions. Due to the massive number of candidate groups and the high index construction consumption, the existing top- k G-Skyline algorithms cannot handle large-scale databases well. In this paper, an efficient algorithm is proposed called the TGPE (T op- k G -Skyline algorithm based on P resorting and E numeration) to rapidly compute the top- k G-Skyline groups for large-scale databases. TGPE proposes an efficient scanning method based on a presorted table to obtain tuples making up G-Skyline groups and their dominant relationships. TGPE does not need to repeatedly reconstruct the index for different skyline criteria. Three computational lemmas are presented based on sorting information and monotonicity, and they are utilized to reduce the number of candidate groups to obtain results rapidly. Finally, extensive experiments on synthetic and real datasets verify that TGPE has significant performance advantages over those of the existing algorithms. [ABSTRACT FROM AUTHOR]

With the enrichment of social platforms, large-scale group decision-making (LSGDM) in social networks has gradually taken shape. In practice, experts may show limited compromise behavior during consensus reaching process (CRP), which may lead to bias or even failure in decision-making results. To tackle this issue, considering limited compromise behavior, this paper proposes a maximum satisfaction consensus (MSC) model, and focusing on LSGDM with fuzzy preference relation in social networks. Firstly, an expert weight determination algorithm integrating the interactive weights and individual weights is constructed. The interactive weights are determined by social influence strength, while the individual weights are measured by the compromise degree of limited compromise behavior and the consistency level of preferences. Subsequently, a grey clustering method is developed to classify experts into subgroups, considering both opinions similarity and limited compromise behavior similarity. In CRP, considering the limited compromise behavior, an MSC model under the limited budget determined by the minimum cost consensus (MCC) model is proposed. Based on MSC and MCC models, satisfaction-cost ratio is defined to measure the efficiency of CRP. Finally, a case study of selecting of shared bicycle operators and further discussion are conducted to verify the feasibility and superiority of the study. [ABSTRACT FROM AUTHOR]

Recently, numerous unsupervised multi-view feature selection methods have been presented. However, these methods assess the significance of data features in each view individually or jointly evaluate the significance of data features across multiple views by concatenating data features from all data views. So, they disregard either inter-view or intra-view feature connections of data. Moreover, they ignore the complementary information from multiple insufficient views, each of which only captures partial information of multi-view data and is insufficient to characterize data distribution. To address these problems, this paper proposes a model named Dual-level Feature Assessment for Unsupervised Multi-view Feature Selection with Latent Space Learning (DFA-LSL). It first investigates the underlying complementary information from multiple views in a latent space while simultaneously learning latent representations for data. Compared with the original data representations, the latent representations contain the entire information of multi-view data. Then, it explores connections and distinguishing ability of features at two levels, namely the inter-view and intra-view levels, by jointly assessing the significance of features in the latent space and individually assessing the significance of features in each view concurrently. Following that, an effective optimization algorithm is presented. Extensive experiments demonstrate that the proposed work surpasses several state-of-the-art models. [ABSTRACT FROM AUTHOR]

This paper is interested in the design of full-order H ∞ memory filters for discrete-time nonlinear systems within finite-time domain. First, the investigated nonlinear systems are modeled by a Takagi–Sugeno fuzzy technique for the interval type-2 case. And to reduce the size of the model, only the past measurements of systems are involved during the memory filters construction. Then, novel design processes with less conservatism are proposed through executing a decoupling transformation for the filtering error dynamics. Based on Lyapunov methods together with several strategies that apply convex optimization techniques and slack variables, some sufficient conditions are presented and certified to solve the memory filter parameters, such that the augmented error dynamics are singular finite-time bounded with prescribed H ∞ performance. Finally, an example is introduced to illustrate the effectiveness of the proposed strategy in contrast to existent works. [ABSTRACT FROM AUTHOR]